EP-SPARQL: A Unified Language for Event   Processing and Stream Reasoning     Darko Anicic1 Paul Fodor2 Sebastian Rudolph3 ...
Introduction                                      EP-SPARQL         Motivation                               Experimental ...
Introduction                                      EP-SPARQL         Motivation                               Experimental ...
Introduction                                      EP-SPARQL         Motivation                               Experimental ...
Introduction                                      EP-SPARQL         Motivation                               Experimental ...
Introduction                                      EP-SPARQL         Motivation                               Experimental ...
Introduction                                      EP-SPARQL         Motivation                               Experimental ...
Introduction                                      EP-SPARQL         Motivation                               Experimental ...
Introduction                                      EP-SPARQL         Motivation                               Experimental ...
Introduction                                    EP-SPARQL         Motivation                             Experimental Resu...
Introduction                                    EP-SPARQL           Motivation                             Experimental Re...
Introduction                                    EP-SPARQL           Motivation                             Experimental Re...
Introduction                                    EP-SPARQL              Motivation                             Experimental...
Introduction                                    EP-SPARQL         Motivation                             Experimental Resu...
Introduction                                                        Syntax                                      EP-SPARQL ...
Introduction                                                        Syntax                                      EP-SPARQL ...
Introduction                                                        Syntax                                      EP-SPARQL ...
Introduction                                                       Syntax                                     EP-SPARQL   ...
Introduction                                                        Syntax                                      EP-SPARQL ...
Introduction                                                        Syntax                                      EP-SPARQL ...
Introduction                                                            Syntax                                          EP...
Introduction                                                            Syntax                                          EP...
Introduction                                                            Syntax                                          EP...
Introduction                                                            Syntax                                          EP...
Introduction                                                            Syntax                                          EP...
Introduction                                                            Syntax                                          EP...
Introduction                                                                                   Test 1: Event Processing   ...
Introduction                                                      Test 1: Event Processing                                ...
Introduction                                                                                       Test 1: Event Processin...
Introduction                                                       Test 1: Event Processing                               ...
Introduction                                                        Conclusion                                      EP-SPA...
Introduction                                                       Conclusion                                     EP-SPARQ...
Introduction                                                  Conclusion                                EP-SPARQL         ...
Introduction                                                  Conclusion                                EP-SPARQL         ...
Introduction                                                  Conclusion                                EP-SPARQL         ...
Introduction                                                  Conclusion                                EP-SPARQL         ...
Introduction                                                  Conclusion                                EP-SPARQL         ...
Upcoming SlideShare
Loading in …5
×

EP-SPARQL: A Unified Language for Event Processing and Stream Reasoning

2,132 views

Published on

A talk given at WWW 2011, Hyderabad, India. See the paper related to this talk: http://sites.google.com/site/darkoanicic/www29-anicic.pdf

Published in: Technology
0 Comments
4 Likes
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
2,132
On SlideShare
0
From Embeds
0
Number of Embeds
10
Actions
Shares
0
Downloads
70
Comments
0
Likes
4
Embeds 0
No embeds

No notes for slide

EP-SPARQL: A Unified Language for Event Processing and Stream Reasoning

  1. 1. EP-SPARQL: A Unified Language for Event Processing and Stream Reasoning Darko Anicic1 Paul Fodor2 Sebastian Rudolph3 Nenad Stojanovic1 1 FZI Research Center for Information Technology, Karlsruhe, Germany 2 State University of New York at Stony Brook, USA 3 Karlsruhe Institute of Technology, Karlsruhe, Germany WWWW 2011, Hyderabad, India
  2. 2. Introduction EP-SPARQL Motivation Experimental Results Related Work ConclusionIntroduction Real-time data appear increasingly today everywhere. How to effectively process this data? Financial sector to find dealing opportunities across available assets to detect fraud and enable real-time surveillance to monitor operational risks Traffic control systems to observe traffic-update events and (re)plan the traffic to route the traffic and optimise paths Sensor networks to process sensor data and detect real-time observations e.g., weather observations like tsunamis, hurricanes etc. D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
  3. 3. Introduction EP-SPARQL Motivation Experimental Results Related Work ConclusionIntroduction Real-time data appear increasingly today everywhere. How to effectively process this data? Financial sector to find dealing opportunities across available assets to detect fraud and enable real-time surveillance to monitor operational risks Traffic control systems to observe traffic-update events and (re)plan the traffic to route the traffic and optimise paths Sensor networks to process sensor data and detect real-time observations e.g., weather observations like tsunamis, hurricanes etc. D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
  4. 4. Introduction EP-SPARQL Motivation Experimental Results Related Work ConclusionIntroduction Real-time data appear increasingly today everywhere. How to effectively process this data? Financial sector to find dealing opportunities across available assets to detect fraud and enable real-time surveillance to monitor operational risks Traffic control systems to observe traffic-update events and (re)plan the traffic to route the traffic and optimise paths Sensor networks to process sensor data and detect real-time observations e.g., weather observations like tsunamis, hurricanes etc. D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
  5. 5. Introduction EP-SPARQL Motivation Experimental Results Related Work ConclusionIntroduction Real-time data appear increasingly today everywhere. How to effectively process this data? Financial sector to find dealing opportunities across available assets to detect fraud and enable real-time surveillance to monitor operational risks Traffic control systems to observe traffic-update events and (re)plan the traffic to route the traffic and optimise paths Sensor networks to process sensor data and detect real-time observations e.g., weather observations like tsunamis, hurricanes etc. D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
  6. 6. Introduction EP-SPARQL Motivation Experimental Results Related Work ConclusionSupporting Real-Time Information Processing An event is something that occurs, happens or changes the current state of affairs. To detect more complex dynamic matters, (simpler) events are combined into complex events. Event Processing deals with the task of processing events with the goal of identifying meaningful situations, using event operators as well as temporal and semantic relationships. D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
  7. 7. Introduction EP-SPARQL Motivation Experimental Results Related Work ConclusionSupporting Real-Time Information Processing An event is something that occurs, happens or changes the current state of affairs. To detect more complex dynamic matters, (simpler) events are combined into complex events. Event Processing deals with the task of processing events with the goal of identifying meaningful situations, using event operators as well as temporal and semantic relationships. D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
  8. 8. Introduction EP-SPARQL Motivation Experimental Results Related Work ConclusionSupporting Real-Time Knowledge Processing Current EP systems provide on-the-fly analysis of data streams, but fall short of combining events with higher-level background knowledge. Background knowledge describes the context or domain in which events are interpreted. Reasoning techniques are necessary for handling background knowledge as events occur! Stream Reasoning deals with the task of conjunctively reasoning over streaming data and background knowledge. D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
  9. 9. Introduction EP-SPARQL Motivation Experimental Results Related Work ConclusionSupporting Real-Time Knowledge Processing Current EP systems provide on-the-fly analysis of data streams, but fall short of combining events with higher-level background knowledge. Background knowledge describes the context or domain in which events are interpreted. Reasoning techniques are necessary for handling background knowledge as events occur! Stream Reasoning deals with the task of conjunctively reasoning over streaming data and background knowledge. D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
  10. 10. Introduction EP-SPARQL Motivation Experimental Results Related Work ConclusionToward Real-Time Semantic Web Rapidly changing data represented as events handles Event Processing (EP) D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
  11. 11. Introduction EP-SPARQL Motivation Experimental Results Related Work ConclusionToward Real-Time Semantic Web Rapidly changing data Static or slowly evolving represented as events background knowledge handles handles Event Processing Semantic Web technologies (EP) including D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
  12. 12. Introduction EP-SPARQL Motivation Experimental Results Related Work ConclusionToward Real-Time Semantic Web Rapidly changing data Static or slowly evolving represented as events background knowledge handles handles Event Processing Semantic Web technologies (EP) including EP SPARQL EP-SPARQL D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
  13. 13. Introduction EP-SPARQL Motivation Experimental Results Related Work ConclusionToward Real-Time Semantic Web Rapidly changing data Static or slowly evolving represented as events background knowledge handles handles Event Processing Semantic Web technologies (EP) including EP SPARQL EP-SPARQL • Temporal relatedness • Semantic relatedness • Stream reasoning D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
  14. 14. Introduction EP-SPARQL Motivation Experimental Results Related Work ConclusionRelated Work Streaming Databases SASE [1] ZStream [10] CEDR [6] TelegraphCQ [5] Temporal (and Spatial) RDF Introducing time into RDF [8] SPARQL-ST [11], Temporal SPARQL [12], stSPARQL [9], and T-SPARQL [7] Stream Reasoning C-SPARQL [3] Streaming Knowledge Bases [13] Streaming SPARQL [4] Incremental reasoning [2] D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
  15. 15. Introduction Syntax EP-SPARQL Semantics Experimental Results Execution Model ConclusionSyntax of the Language EP-SPARQL – extends SPARQL to enable event-based processing that takes into account temporal situatedness of triple assertions. – syntactical and semantic downward-compatibility to plain SPARQL. 1 Operators: FILTER , AND, UNION , OPTIONAL , SEQ , EQUALS , OPTIONALSEQ , and EQUALSOPTIONAL ; 2 getDURATION() yields a literal of type xsd:duration giving the time interval associated to the graph pattern; 3 getSTARTTIME() and getENDTIME() retrieve the time stamps of type xsd:dateTime of the start and end of the interval; D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
  16. 16. Introduction Syntax EP-SPARQL Semantics Experimental Results Execution Model ConclusionSyntax of the Language EP-SPARQL – extends SPARQL to enable event-based processing that takes into account temporal situatedness of triple assertions. – syntactical and semantic downward-compatibility to plain SPARQL. 1 Operators: FILTER , AND, UNION , OPTIONAL , SEQ , EQUALS , OPTIONALSEQ , and EQUALSOPTIONAL ; 2 getDURATION() yields a literal of type xsd:duration giving the time interval associated to the graph pattern; 3 getSTARTTIME() and getENDTIME() retrieve the time stamps of type xsd:dateTime of the start and end of the interval; D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
  17. 17. Introduction Syntax EP-SPARQL Semantics Experimental Results Execution Model ConclusionInformal Semantics RDF stream – a set of triple occurrences s, p, o , tα , tω where s, p, o is an RDF triple and tα , tω are the start and end of the interval. FILTER – restricts variable bindings to those µ, tα , tω for which the filter expression evaluates to true; AND – joins µ, tα , tω and µ , tα , tω . The joined tuple has timestamp tα = min(tα , tα ), tω = max(tω , tω ); UNION – forms the disjunction of µ, tα , tω and µ , tα , tω ; OPTIONAL – matches µ, tα , tω optionally with µ , tα , tω when the filter expression evaluates to true; D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
  18. 18. Introduction Syntax EP-SPARQL Semantics Experimental Results Execution Model ConclusionInformal Semantics (cont’d) SEQ – joins µ, tα , tω and µ , tα , tω only if µ , tα , tω occurs strictly after µ, tα , tω ; EQUALS – joins µ, tα , tω and µ , tα , tω if they occur simultaneously; OPTIONALSEQ and EQUALSOPTIONAL are temporal-sensitive variants of OPTIONAL; CONSTRUCT – generates the stream enriched by triples from possibly iterative CONSTRUCT rules. SELECT-queries get evaluated not against the pure input stream but against the enriched generated stream. D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
  19. 19. Introduction Syntax EP-SPARQL Semantics Experimental Results Execution Model ConclusionExample I Continuously search for companies having a larger than 20% stock price increase in less than 15 days without having acquired another company during that period. SELECT ?company WHERE { ?company hasStockprice ?price1 } SEQ { { ?company hasAcquired ?othercompany } OPTIONALSEQ { ?company hasStockPrice ?price2 } } FILTER ( ?price2 > ?price1 * 1.2 && !BOUND(?othercompany) && getDURATION() < "P15D"ˆˆxsd:duration) D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
  20. 20. Introduction Syntax EP-SPARQL Semantics Experimental Results Execution Model ConclusionExample II Detect slow traffic and its cause which could be used to automatically modify a speed limit on a certain roads. SELECT ?road ?speed WHERE { ?road tr: slowTrafficDue ?observ } SEQ {{ ?road tr: slowTrafficDue ?observ } { ?observ rdfs:subClassOf tr:SlowTraffic } { ?observ wt:speed ?speed }} FILTER ( getDURATION() < "P1H"ˆˆxsd:duration) Observ_1 Observ_2 rdf:type tr:GhostDriver ; rdf:type tr:IceConditions ; wt:speed "50"ˆˆxsd:int . wt:speed "40"ˆˆxsd:int . tr:GhostDriver rdfs:subClassOf tr:SlowTraffic. tr:IceConditions rdfs:subClassOf tr:BadWeather. D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
  21. 21. Introduction Syntax EP-SPARQL Semantics Experimental Results Execution Model ConclusionSequence Operator SELECT ?company WHERE { ?comp hasStockPrice ?pr1 } SEQ { ?comp hasStockPrice ?pr2 } SEQ { ?comp hasStockPrice ?pr3 } s, p, o , ti , tj represented as triple(s, p, o, Ti , Tj ), and τ represents s, p, o. triple(τi , T1 , T4 ) ← triple(τ1 , T1 , T2 ) SEQ triple(τ2 , T3 , T4 ). triple(τ, T1 , T6 ) ← triple(τi , T1 , T4 ) SEQ triple(τ3 , T5 , T6 ). Rule transformation – Incremental computation (Prolog syntax) triple(τ1 , T1 , T2 ) :- assert goal(triple(τ2 , , ), triple(τ1 , T1 , T2 ), triple(τi , , )) . triple(τ2 , T3 , T4 ) : − goal(triple(τ2 , , ), triple(τ1 , T1 , T2 ), triple(τ, , )), T2 < T3 , retract goal(triple(τ2 , , ), triple(τ1 , T1 , T2 ), triple(τi , , )) , triple(τi , T1 , T4 ). D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
  22. 22. Introduction Syntax EP-SPARQL Semantics Experimental Results Execution Model ConclusionSequence Operator SELECT ?company WHERE { ?comp hasStockPrice ?pr1 } SEQ { ?comp hasStockPrice ?pr2 } SEQ { ?comp hasStockPrice ?pr3 } s, p, o , ti , tj represented as triple(s, p, o, Ti , Tj ), and τ represents s, p, o. triple(τi , T1 , T4 ) ← triple(τ1 , T1 , T2 ) SEQ triple(τ2 , T3 , T4 ). triple(τ, T1 , T6 ) ← triple(τi , T1 , T4 ) SEQ triple(τ3 , T5 , T6 ). Rule transformation – Incremental computation (Prolog syntax) triple(τ1 , T1 , T2 ) :- assert goal(triple(τ2 , , ), triple(τ1 , T1 , T2 ), triple(τi , , )) . triple(τ2 , T3 , T4 ) : − goal(triple(τ2 , , ), triple(τ1 , T1 , T2 ), triple(τ, , )), T2 < T3 , retract goal(triple(τ2 , , ), triple(τ1 , T1 , T2 ), triple(τi , , )) , triple(τi , T1 , T4 ). D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
  23. 23. Introduction Syntax EP-SPARQL Semantics Experimental Results Execution Model ConclusionSequence Operator SELECT ?company WHERE { ?comp hasStockPrice ?pr1 } SEQ { ?comp hasStockPrice ?pr2 } SEQ { ?comp hasStockPrice ?pr3 } s, p, o , ti , tj represented as triple(s, p, o, Ti , Tj ), and τ represents s, p, o. triple(τi , T1 , T4 ) ← triple(τ1 , T1 , T2 ) SEQ triple(τ2 , T3 , T4 ). triple(τ, T1 , T6 ) ← triple(τi , T1 , T4 ) SEQ triple(τ3 , T5 , T6 ). Rule transformation – Incremental computation (Prolog syntax) triple(τ1 , T1 , T2 ) :- assert goal(triple(τ2 , , ), triple(τ1 , T1 , T2 ), triple(τi , , )) . triple(τ2 , T3 , T4 ) : − goal(triple(τ2 , , ), triple(τ1 , T1 , T2 ), triple(τ, , )), T2 < T3 , retract goal(triple(τ2 , , ), triple(τ1 , T1 , T2 ), triple(τi , , )) , triple(τi , T1 , T4 ). D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
  24. 24. Introduction Syntax EP-SPARQL Semantics Experimental Results Execution Model ConclusionOther Operators SELECT ?company WHERE ... FILTER ( ?price2 < ?price1 * 0.7 && ?price3 > ?price1 * 1.05) FILTER – Rule transformation condition(Price1, Price2, Price3) : − P1 is (Price1 ∗ 0.7), P1 >Price2, P2 is (Price1 ∗ 0.5), Price3>P2 . EQUALS – Rule transformation equals(TI1 , TI2 ) : − TI1 = [TI1 S, TI1 E], validTimeInterval(TI1 ), TI2 = [TI2 S, TI2 E], validTimeInterval(TI2 ), TI1 S = TI2 S, TI1 E = TI2 E. validTimeInterval(TI) ← TI = [TI S, TI E], TI S@ < TI E. D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
  25. 25. Introduction Syntax EP-SPARQL Semantics Experimental Results Execution Model ConclusionOther Operators SELECT ?company WHERE ... FILTER ( ?price2 < ?price1 * 0.7 && ?price3 > ?price1 * 1.05) FILTER – Rule transformation condition(Price1, Price2, Price3) : − P1 is (Price1 ∗ 0.7), P1 >Price2, P2 is (Price1 ∗ 0.5), Price3>P2 . EQUALS – Rule transformation equals(TI1 , TI2 ) : − TI1 = [TI1 S, TI1 E], validTimeInterval(TI1 ), TI2 = [TI2 S, TI2 E], validTimeInterval(TI2 ), TI1 S = TI2 S, TI1 E = TI2 E. validTimeInterval(TI) ← TI = [TI S, TI E], TI S@ < TI E. D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
  26. 26. Introduction Syntax EP-SPARQL Semantics Experimental Results Execution Model ConclusionOther Operators SELECT ?company WHERE ... FILTER ( ?price2 < ?price1 * 0.7 && ?price3 > ?price1 * 1.05) FILTER – Rule transformation condition(Price1, Price2, Price3) : − P1 is (Price1 ∗ 0.7), P1 >Price2, P2 is (Price1 ∗ 0.5), Price3>P2 . EQUALS – Rule transformation equals(TI1 , TI2 ) : − TI1 = [TI1 S, TI1 E], validTimeInterval(TI1 ), TI2 = [TI2 S, TI2 E], validTimeInterval(TI2 ), TI1 S = TI2 S, TI1 E = TI2 E. validTimeInterval(TI) ← TI = [TI S, TI E], TI S@ < TI E. D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
  27. 27. Introduction Test 1: Event Processing EP-SPARQL Test 2: Stream Reasoning Experimental Results Test 3: Example applications ConclusionEvent Processing Test pattern: monitoring Throughput vs. Window size Throughput vs. Window size (Sum over Sequence) Esper 3.3.0 EALIS-YAP Throughput (1000 x Events/Sec) ETALIS-YAP Esper 10000 the average stock price 30 of a company X withThroughput (Event/Sec) 25 9000 8000 CONSTRUCT queries 20 15 7000 6000 Intel Core Quad CPU 10 5000 Q9400 2,66GHz, 8GB of 5 0 4000 RAM, Ubuntu 9.10 1 3 10 100 500 1000 50000 Count window size Time window size (Sec) ETALIS on SWI Prolog 5.6.64 and YAP Prolog Figure: Aggregation over count 5.1.3 vs. Esper 3.3.0 sliding window D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
  28. 28. Introduction Test 1: Event Processing EP-SPARQL Test 2: Stream Reasoning Experimental Results Test 3: Example applications ConclusionStream Reasoning !"#$"%$&()*+,$-,#$.+/,"0(01$2+*/3 %&()*+,- $!!! Test pattern: infer over .+/,"0(01$2+*/3$(0$5, #"!! streaming triples whether the subject of a #!!! triple is an instance of "!! the class of concern, ! "!!! #!!!! #"!!! $!!!! or any of its 40,080 !456+$"%$&()*+, subclasses. Figure: Delay caused by stream reasoning D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
  29. 29. Introduction Test 1: Event Processing EP-SPARQL Test 2: Stream Reasoning Experimental Results Test 3: Example applications Conclusion Example application I No. of Locations vs. Consumed Memory No. of Locations vs. Consumed Time 1 Visitor 10 Visitors 1 Visitor 10 Visitors 2000 Goods Delivery System 1500 1295 Consumed time in ms in the city of Milan;Consumed Memory in kB 1503 1076 1500 1000 889 RDF knowledge base to 1039 1000 810 represent locations and 500 500 traffic links; 204 86 119 165 31 94 109 130 35 3 0 0 The system “listens” to 5 10 15 20 5 10 15 20 Number of locations Number of locations traffic-update events and reschedule paths. Figure: Delay caused by processing D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
  30. 30. Introduction Test 1: Event Processing EP-SPARQL Test 2: Stream Reasoning Experimental Results Test 3: Example applications ConclusionExample application II Pressure difference Threshold 0.08 A tsunami detection 0.06 system; 0.04 Real buoy sensor data Pressure 0.02 continuously processed; 0 GeoNames to provide -0.02 geographical places -0.04 Time within a certain radius from the sensor location. Figure: Tsunami detection histogram D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
  31. 31. Introduction Conclusion EP-SPARQL Example Experimental Results Semantics ConclusionConclusion 1 Addressing dynamics and notification on the Web has become an important area of research; 2 The challenge is to get advantage of real-time data, and recognise important situations in a timely fashion; 3 EP-SPARQL – a new language for Event Processing and Stream Reasoning; 4 Future work: complete implementation, more expressive formalisms, and adaptive optimizations. D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
  32. 32. Introduction Conclusion EP-SPARQL Example Experimental Results Semantics ConclusionThank you! Questions... ETALIS is open source: http://code.google.com/p/etalis On-line demo: http://etalis.fzi.de Contact: Darko.Anicic@fzi.de D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
  33. 33. Introduction Conclusion EP-SPARQL Example Experimental Results Semantics ConclusionJagrati Agrawal, Yanlei Diao, Daniel Gyllstrom, and NeilImmerman.Efficient pattern matching over event streams.In Proceedings of the 28th ACM SIGMOD Conference,pages 147–160, 2008.Davide Francesco Barbieri, Daniele Braga, Stefano Ceri,Emanuele Della Valle, and Michael Grossniklaus.Incremental reasoning on streams and rich backgroundknowledge.In Proceedings of the 7th Extended Semantic WebConference (ESWC’10), pages 1–15, 2010.Davide Francesco Barbieri, Daniele Braga, Stefano Ceri,and Michael Grossniklaus.An execution environment for C-SPARQL queries. D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
  34. 34. Introduction Conclusion EP-SPARQL Example Experimental Results Semantics ConclusionIn Proceedings of the 13th International Conference onExtending Database Technology (EDBT’10), pages441–452, 2010.Andre Bolles, Marco Grawunder, and Jonas Jacobi.Streaming SPARQL - extending SPARQL to process datastreams.In Proceedings of the 5th European Semantic WebConference (ESWC’08), pages 448–462, 2008.Sirish Chandrasekaran, Owen Cooper, Amol Deshpande,Michael J. Franklin, Joseph M. Hellerstein, Wei Hong,Sailesh Krishnamurthy, Samuel Madden, VijayshankarRaman, Frederick Reiss, and Mehul A. Shah.Telegraphcq: Continuous dataflow processing for anuncertain world. D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
  35. 35. Introduction Conclusion EP-SPARQL Example Experimental Results Semantics ConclusionIn Proceedings of the 1st Biennial Conference onInnovative Data Systems Research (CIDR’03), 2003.Mitch Cherniack, Hari Balakrishnan, MagdalenaBalazinska, Donald Carney, Ugur Cetintemel, Ying Xing, ¸and Stanley B. Zdonik.Scalable distributed stream processing.In Proceedings of the 1st Biennial Conference onInnovative Data Systems Research (CIDR’03), 2003.Fabio Grandi.T-SPARQL: a TSQL2-like temporal query language forRDF.In International Workshop on on Querying GraphStructured Data, pages 21–30, 2010.Claudio Gutierrez, Carlos A. Hurtado, and Alejandro A.Vaisman. D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
  36. 36. Introduction Conclusion EP-SPARQL Example Experimental Results Semantics ConclusionIntroducing time into rdf.The IEEE Transactions on Knowledge and DataEngineering, 19(2):207–218, 2007.Manolis Koubarakis and Kostis Kyzirakos.Modeling and querying metadata in the semantic sensorweb: The model stRDF and the query language stSPARQL.In Proceedings of the 7th Extended Semantic WebConference (ESWC’10), pages 425–439, 2010.Yuan Mei and Samuel Madden.Zstream: a cost-based query processor for adaptivelydetecting composite events.In Proceedings of the 29th ACM SIGMOD Conference,pages 193–206, 2009.Matthew Perry, Amit P. Sheth, and Prateek Jain. D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
  37. 37. Introduction Conclusion EP-SPARQL Example Experimental Results Semantics ConclusionSPARQLST: Extending SPARQL to support spatiotemporalqueries.In Technical Report. KNOESIS-TR-2009-01, 2008.Jonas Tappolet and Abraham Bernstein.Applied temporal RDF: Efficient temporal querying of RDFdata with SPARQL.In Proceedings of the 6th European Semantic WebConference (ESWC’09), pages 308–322, 2009.Onkar Walavalkar, Anupam Joshi, Tim Finin, and YelenaYesha.Streaming knowledge bases.In International Workshop on Scalable Semantic WebKnowledge Base Systems, 2008. D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011

×